Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network
Abstract
This paper presents a method for speech emotion recognition using spectrograms and deep convolutional neural network (CNN). Spectrograms generated from the speech signals are input to the deep CNN. The proposed model consisting of three convolutional layers and three fully connected layers extract discriminative features from spectrogram images and outputs predictions for the seven emotions. In this study, we trained the proposed model on spectrograms obtained from Berlin emotions dataset. Furthermore, we also investigated the effectiveness of transfer learning for emotions recognition using a pre-trained AlexNet model. Preliminary results indicate that the proposed approach based on freshly trained model is better than the fine-tuned model, and is capable of predicting emotions accurately and efficiently.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some studies considered using known architectures from object recognition and regarded it as necessary to pre-train these larger models on ImageNet to avoid overfitting the relatively few labeled examples.
“Some considered using known architectures from object recognition [132], [85], [127]. Here, the authors regarded it as necessary to pre-train these larger models on the ImageNet database to avoid overfitting the relatively few labeled examples.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [127]
The citation lists haven’t been fetched yet
References 22 · Cited by 413
- Authors
- Badshah, A. M., Ahmad, J., Rahim, N., & Baik, S. W.
- Affiliations
- Sejong University (KR)
- Venue
- 2017 International Conference on Platform Technology and Service (PlatCon)
- Publisher
- IEEE
- Published
- 2017-02
- Pages
- 1-5
- OpenAlex
- W2598207902
- Cited by
- 413
- References
- 22
- Citations by year
- 2026: 10 · 2025: 30 · 2024: 57 · 2023: 65 · 2022: 57 · 2021: 79 · 2020: 61 · 2019: 32 · 2018: 16 · 2017: 6
- FWCI
- 36.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Platform Technology and Service (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- speech emotion recognition, spectrogram, deep convolutional neural networks, transfer learning, AlexNet, fine-tuning, emotion classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
